Relationship detection within biometric match results candidates

ABSTRACT

Systems and methods for enhancing confidence in a biometric search result include submitting one or more biometric samples to a biometric search engine. In response to the one or more submitted biometric samples, a plurality of candidates identified as potentially associated with the one or more submitted biometric samples is received from the biometric search engine. Each identified candidate has associated biographic information. The biographic information associated with each identified candidate is submitted to a relationship detection engine. In response to the submitted biographic information, an identified relationship between at least one of the identified candidates and one or more other individuals is received from the relationship detection engine.

RELATED APPLICATION

This application is a Continuation of U.S. patent application Ser. No.13/988,057, filed May 17, 2013, now U.S. Pat. No. 9,984,157, which is anational stage application under 35 U.S.C. 371 of PCT Application No.PCT/US2011/062770, having an international filing date of Dec. 1, 2011,which designated the United States, which PCT application claims thebenefit of U.S. Provisional Application No. 61/418,573, filed on Dec. 1,2010, titled “Relationship Detection within Biometric Match ResultsCandidates,” each of which are incorporated herein by reference in theirentirety.

FIELD OF THE INVENTION

The invention relates generally to biometric systems used for personidentification. More specifically, the invention relates to biometricsystems and methods that detect relationships between biometric matchresults candidates to improve confidence in person identifications.

BACKGROUND

Biometric databases can be useful for criminal investigation. Abiometric database comprises records, each of which contains biometricsample data and associated biographic data. For example, when booking acriminal suspect, a police agency collects biometrics (e.g.,fingerprints, iris, “mug shot”, DNA,) and biographic information, suchas a name, address, height, weight, etc. The collected biometrics andbiographics are stored together as a database record. Further, acriminal investigation may recover latent biometric samples from a crimescene, which are submitted later to an automated biometricidentification system (ABIS). The biometric identification systemalgorithmically compares the latent biometric samples to records in thebiometric database in an attempt to ascertain the source individual orindividuals of the latent biometric samples.

The mechanism of biometric matching, however, is probabilistic innature, and does not attempt to yield 100% certainty in identification.For example, a biometric search typically does not yield a certainmatch, but rather a ranked candidate list, which highly trained forensicspecialists then review manually. The biometric search results, however,can be inconclusive, particularly when the latent biometric sample is ofpoor quality.

SUMMARY

In one aspect, the invention features a method of enhancing confidencein a biometric search result. The method comprises submitting one ormore biometric samples to a biometric search engine. In response to theone or more submitted biometric samples, a plurality of candidatesidentified as potentially associated with the one or more submittedbiometric samples is received from the biometric search engine. Eachidentified candidate has biographic information associated therewith.The biographic information associated with each identified candidate issubmitted to a relationship detection engine. In response to thesubmitted biographic information, an identified relationship between atleast one of the identified candidates and one or more other individualsis received from the relationship detection engine.

In another aspect, the invention features a computer system comprisingmemory storing an application program, and a processor running theapplication program stored in the memory. The application program isconfigured to submit one or more biometric samples to a biometric searchengine, to receive from the biometric search engine, in response to theone or more submitted biometric samples, a plurality of candidatespotentially associated with the one or more biometric samples, to submitbiographic information associated with each identified candidate to arelationship detection engine, and to receive from the relationshipdetection engine, in response to the submitted biographic information,an identified relationship between at least one of the identifiedcandidates and one or more other individuals.

In still another aspect, the invention features a computer programproduct for enhancing confidence in a biometric search result. Thecomputer program product comprises a computer readable storage mediumhaving computer readable program code embodied therewith. The computerreadable program code comprising computer readable program codeconfigured to submit one or more biometric samples to a biometric searchengine, computer readable program code configured to receive from thebiometric search engine, in response to the one or more submittedbiometric samples, at least one candidate identified as potentiallyassociated with the one or more biometric samples. Each identifiedcandidate has biographic information associated therewith. The computerreadable program code further comprises computer readable program codeconfigured to submit the biographic information associated with eachidentified candidate to a relationship detection engine, and computerreadable program code configured to receive from the relationshipdetection engine, in response to the submitted biographic information,an identified relationship between at least two of the biometricallyidentified candidates.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of this invention may be betterunderstood by referring to the following description in conjunction withthe accompanying drawings, in which like numerals indicate likestructural elements and features in various figures. The drawings arenot necessarily to scale, emphasis instead being placed uponillustrating the principles of the invention.

FIG. 1 is block diagram of an embodiment of a biometric identificationsystem.

FIG. 2 is a block diagram showing use of the biometric identificationsystem to enhance confidence in a biometric search result by detectingrelationships between one or more candidates (biometrically identifiedas being associated with a biometric sample) and a person of interest.

FIG. 3 is a flow diagram of an embodiment of a process for enhancingconfidence in a biometric search result by detecting relationshipsbetween one or more candidates and a person of interest.

FIG. 4 is a diagram illustrating levels of associations for candidatesand persons of interest.

FIG. 5 is a diagram illustrating an example of a relationship detectedbetween a person of interest and a candidate.

FIG. 6 is a block diagram showing another use of the biometricidentification system to enhance confidence in a biometric search resultby detecting relationships between candidates associated with differentbiometric samples collected under related circumstances or events.

FIG. 7 is a flow diagram of an embodiment of another process forenhancing confidence in a biometric search result.

DETAILED DESCRIPTION

Automated biometric systems described herein integrate biometricidentification technologies with relationship detection technologies toimprove confidence in biometric search results. Applicant recognizedthat the biographics associated with a biometric match do notnecessarily improve the confidence in the biometrics match (e.g., suchbiographic information could be false, incorrect, conflicting,ambiguous, outdated, etc.). Notwithstanding, the applicant alsorecognized another application for the biographics that could improveconfidence in the biometric results, namely, through relationshipdetection.

In general, relationship detection is a mechanism that employs publicand private data records in text-based databases to identify non-obviousrelationships between individuals. The biographics of biometric matchcandidates and, in some instances, victims can be used to search theserelationship-detection databases for relationships between candidatesand between candidates and victims. Any detected relationships providecontext for the biometric search that identified the candidates, therebyhelping interpret what could be an otherwise inconclusive biometricsearch. Advantageously, the integration of biometric identification withrelationship detection operates to lower error rates (missedidentifications and misidentifications), to enable working with largerpopulation sizes, to improve robustness with respect to missing ordegraded biometric samples, to increase security with respect tospoofing, and to reduce the failure-to-enroll rate.

FIG. 1 shows a functional block diagram of an embodiment of a biometricsystem 2 including a computer system 10 configured to improve theconfidence in a biometric result as described herein. Exampleimplementations of the computer system 10 include, but are not limitedto, computers (PCs and Macintosh), workstations, servers, hand-helddevices, such as personal digital assistants (PDA), cellular phones,smartphones, Apple iPods™ and iPads™ Amazon KINDLES®, mobile phones,kiosks, and network terminals. The computer system 10 generallycomprises a hardware layer 12 and a software layer 14.

The hardware layer 12 includes a processor 16, memory 18, storage 20, adisplay screen 22, user input device(s) 24, and a network interface 26.The processor 16 can be a proprietary or conventional cache-basedmachine, employing hardware logic, software logic, or a combinationthereof in the performance of its processing tasks, such as memoryaccess, communication-related processing, arithmetic/logical operations,and control.

The memory 18 includes non-volatile computer storage media, such asread-only memory (ROM), and volatile computer storage media, such asrandom-access memory (RAM). Typically stored in the ROM is a basicinput/output system (BIOS), which contains program code for controllingbasic operations of the computing system 10 including start-up of thecomputing device and initialization of hardware. Stored typically withinthe RAM are data and executing program code, such as applicationprograms and program modules (e.g., DLLs or dynamic link libraries).

Storage 20 includes internal or external (or both) persistent storagedevices, such as hard disk drives, SATA (serial advanced technologyattachment), USB (Universal Serial Bus) devices, and network attachedstorage (NAS). The storage 20 can be used for persistent storage ofdata, databases, and files.

The display screen 22 is an electronic visual display for the computersystem 10. Depending on the type of the computer system 10, embodimentsof the computer screen 22 include, but are not limited to, a monitor,flat-panel screen, and touch-screen.

In general, the user-input device 24 can be any peripheral device usedto provide data and control signals to the computer system 10. Exampleembodiments of the user-input device(s) include, but are not limited to,a keyboard, a mouse, trackball, touch-pad, touch-screen, graphicstablet, microphone, light pen, joystick, heat-mapping devices, andeye-gaze tracking devices.

The network interface 26 connects the computer system 10 to a network40, which may be a local area network, a wide-area network such as theInternet or World Wide Web. The computer system 10 can connect to thenetwork 40 through one of a variety of connections, such as standardtelephone lines, digital subscriber line (DSL), asynchronous DSL, LAN orWAN links (e.g., T1, T3), broadband connections (Frame Relay, ATM), andwireless connections (e.g., 802.11(a), 802.11(b), 802.11(g), 802.11(n)).

A signal bus 28 interconnects the various hardware components of thecomputer system 10. In addition, the signal bus 28 can connect tovarious other components (not shown) of the computer system 10including, for example, a memory interface, a peripheral interface(e.g., to a printer, to a CD-ROM drive), and a video interface. Althoughshown as a single bus 28, the signal bus 28 can comprise multipleseparate busses. Example implementations of the signal bus 28 include,but are not limited to, a Peripheral Component Interconnect (PCI) bus,an Industry Standard Architecture (ISA) bus, an Enhanced IndustryStandard Architecture (EISA) bus, and a Video Electronics StandardsAssociation (VESA) bus.

The software layer 14 includes an application program 30 configured toprovide a biometric services platform, as described herein. Theapplication program 30 is configured to communicate with a biometricssearch engine 32 for performing biometric searches based on submittedbiometric samples. The biometrics search engine 32 can be embodied in anautomated biometric identification system (ABIS). An exampleimplementation of an ABIS system is the Department of DefenseNext-Generation ABIS (NG-ABIS). In the embodiment shown in FIG. 1, theapplication program 30 communicates with the biometrics search engine 32over the network 40; in other embodiments, the biometrics search engine32 can be part of the application program 30.

The biometrics search engine 32 is in communication with one or morebiometric records databases 42 containing the biometric records ofnumerous subjects from whom biometric data, such as fingerprints, DNA,iris images, has been collected. An example implementation of thebiometrics records database 42 is the Department of Defense's AutomatedBiometric Identity System (ABIS) database.

The application program 30 is also configured to communicate with arelationship-detection engine 34 for searching for relationships amongindividuals based on submitted biographic information. An exampleimplementation of the relationship-detection engine 34 is the IdentityResolution Engine 2.2, produced by Infoglide Software of Austin, Tex. Inthe embodiment shown in FIG. 1, the application program 30 communicateswith the relationship-detection engine 34 over the network 40; in otherembodiments, the relationship-detection engine 34 can be part of theapplication program 30.

The relationship-detection engine 34 is in communication with one ormore databases 44 (public, private, or both) containing text-basedrecords of individuals. Examples of such records include, but are notlimited to, addresses, telephone directories, telephone call records,bank records, voter registration records, credit records, criminalrecords, marriage records, tax records, social security databases,social media data, and other publicly posted information thatestablishes links between individuals.

FIG. 2 and FIG. 3 show an embodiment of a process 100 for enhancingconfidence in a biometric search result by detecting relationshipsbetween one or more candidates (biometrically identified as beingassociated with a biometric sample) and a person of interest. Consider,for example, that a single latent biometric sample or sample set (e.g.,a digitized image of a fingerprint) is acquired from a crime scene witha known victim (i.e., the person of interest). This latent biometricsample can be of low quality, leading a biometric search of thebiometric database 42 to yield inconclusive results (e.g., numerouscandidates with similar match scores).

A user of the computer system 10 runs the application program 30 andsubmits (step 102) the sample 50, exemplified in FIG. 2 as afingerprint. The application program 30 communicates with the biometricssearch engine 32 to perform a search based on this biometric sample 50,and the biometrics search engine 32 communicates with the biometricrecords database 42 to perform (step 104) the search. The search of thebiometrics database 42 produces a list 60 of multiple records havingmatch scores above a threshold. Such match scores correspond to thelikelihood of a match being found for the sample 50. Each record isassociated with a candidate, namely, a possible source of the biometricsample, and contains biographic information about the candidate. In thisexample, the list 60 includes six match candidates 62-1, 62-2, 62-3,62-4, 62-5, and 62-6 (generally 62), which can be ranked in order frombest to worst matches. A resulting list 60 can have more or fewer thanthe six candidates shown. The biometrics search engine 32 returns thelist 60 to the application program 30, which displays (step 106) thelist 60 to the user. The user can visually review and possibly edit thislist (e.g., to remove a candidate).

The application program 30 submits this list 60 of candidates 62, andtext-based biographic information associated with these candidates, tothe relationship detection engine 34. In addition to the list 60, theuser submits (step 108) biographic information about a person ofinterest 64 (e.g., the victim) to the relationship detection engine 34.Such biographic information can include, but not be limited to, suchdata as name, date of birth, physical characteristics, such as eyecolor, hair color, height and weight, phone bills, residence records,and names of family members. Based on data records stored in theprivate/public databases 44, the submitted biographics of the POI 64,and the submitted biographics of the candidates 62 in the list 60, therelationship detection engine 34 searches for (step 110) relationships(links) between any of the candidates 62 in the list 60 and the POI 64.The relationship detection engine 34 returns ranked matches indicatingthe presence (i.e., existence), nature (e.g., they share an address),and probability (i.e., confidence level) of an association between oneor more candidates in the list with the POI 64. The application program30 displays (step 112) the result of the relationship detection searchon the display screen 22. In this example, a relationship is discoveredbetween the candidate 62-5 in the list 60 and the POI 64. The use ofrelationship detection thus enhances the biometric search results (i.e.,improves the level of confidence in the results) by singling out asingle candidate (here, e.g., candidate 62-5) who has a relationshipwith the POI 64.

The scope of relationship detection can extend beyond looking for directlinks between candidates 62 and the POI 64. For instance, FIG. 4 showsan oversimplified example of a social network for each candidate 62 andperson of interest 64. Each candidate 62 and POI 64 has one or morefirst-order associates 120 (e.g., relatives, individuals personallyknown or interacted with) and second-order associates 122 (i.e.,first-order associates of the first-order associates 120). Some of thefirst-order associates 120 may have a first-order relationship with eachother, as denoted by link 124, or share a common first-order associate,as denoted by links 126. The identities of known associates of the POI64 may be acquired from various sources, examples of which include butare not limited to a laptop, a cell phone, and cell phone call records.The identities of known associates of the candidates 62 can be obtainedfrom the relationship detection databases 44 and/or from the biographicinformation associated with the candidates in the list 60 produced bythe biometrics search engine 32.

In some embodiments, the relationship detection process can also lookfor relationships between known associates (first order, second order,etc) of the POI 64 and each candidate 62 in the list 60, between the POI64 and any of the known associates of the candidates 62, between anyknown associate of the POI 64 and any known associate of the candidates62, or any combination thereof. The number of levels of associationinvolved in the relationship detection process can be fewer or more thantwo. In general, the strength of a relationship can be inverselyproportional to the degree of separation from the POI 64 or candidate 62at which the relationship is detected.

FIG. 5 shows a simple example of a detected relationship between the POI64 and the candidate 62-5, the relationship being established through afirst-order associate 128 of the POI 64. In this example, thefirst-order associate 128 and the candidate 62-5 share a commonality130, for example, they have the same home address. This commonality 130links the POI 64 and the candidate 62-5 (through the associate 128).Various types of commonalities can link individuals, examples of whichinclude, but are not limited to, shared telephone calls, a commonaddress of residence, place of business, a common employer, telephonenumbers, attendance at the same high school or university, andregistered for the same events, credit card purchases at the samestores, on the same or adjacent dates.

The application program 30 can produce a graphical representation ofthis relationship in a manner that facilitates an understanding of itsexistence and nature (e.g., by displaying the names of the individualsin the relationship, drawing links between the levels of association inthe relationship, by identifying each commonality that establishes therelationship). Based on this identified relationship, a user canconsider the candidate 62-5 to be the likeliest source of the biometricsample 50 leading to the candidate list 60.

FIG. 6 and FIG. 7 show another embodiment of a process 140 for enhancingconfidence in a biometric search result by detecting relationshipsbetween candidates associated with different biometric samples collectedunder related circumstances or events. Consider, for example, multiplelatent biometric samples or sample sets are collected from a crimescene, such as a meeting place for illegal activity. In this example,there may be no known victim, and the criminals are unknown. Again, thelatent biometric samples can be of low quality, leading a biometricsearch of the biometric database 42 to yield inconclusive results foreach of the samples.

A user of the computer system 10 runs the application program 30 andsubmits (step 142) the collected biometric samples 50-1, 50-2, 50-N(generally, 50), exemplified in FIG. 6 as fingerprints. The applicationprogram 30 submits the samples 50 to the biometrics search engine 32with instructions to search for matches for each of the samples 50. Thebiometrics search engine 32 searches (step 144) the biometrics database42, producing lists 60-1, 60-2, 60-N (generally, 60) of candidates, onelist 60 for each sample 50 (e.g., list 60-1 is associated with sample50-1; list 60-2 is associated with sample 50-2). Each candidate 62 ineach list 60 produces a matching score above a specified threshold for amatch. The biometrics search engine 32 returns the lists 60 to theapplication program 30, and the application program 30 displays (step146) the lists 60 of candidates on the display screen 22. Each list 60can have more or fewer candidates than the six match candidates shown.The user can visually review and possibly edit this list (e.g., toremove a candidate).

The application program 30 submits (step 148) the lists 60 of candidates62, and text-based biographic information associated with thesecandidates, to the relationship detection engine 34. The biographicinformation associated with each candidate can include, but not belimited to, the name, date of birth, phone bills, residence records, andnames of family members of that candidate. Based on data records storedin the private/public databases 44 and the submitted biographics foreach candidate 62 in each list 60, the relationship detection engine 34searches for (step 150) relationships (links) between any two candidates62 who are members of different lists. For example, the relationshipdetection engine 34 looks for a relationship between each candidate whois a member of list 60-1 and each candidate in the list 60-2, and alsolooks for a relationship between each candidate in list 60-1 and eachcandidate in list 60-N. Accordingly, if, for example, list 60-1 has tencandidates, list 60-2 has 5 candidates, and list 60-N has twentycandidates, then the relationship detection engine 34 searches forrelationships between 50 (10×5) different pairs of candidates in lists60-1 and 60-2, and 200 (10×20) different pairs of candidates in lists60-1 and 60-N.

The relationship detection engine 34 returns ranked matches indicatingthe presence, nature, and probability of an association betweencandidates having a detected relationship. The application program 30displays (step 152) the results of the relationship detection search onthe display screen 22. In this example, a relationship 160 is detectedbetween the candidate 62-5 in the list 60-1 and the candidate 62′-2 inthe list 60-2. Although one detected relationship is shown, it is to beunderstood that the relationship detection engine 34 can uncover morethan one relationship between candidates of different lists.

As described previously, the relationship detection process can beexpanded to encompass associates, that is, to look for relationships:between known associates (first order, second order, etc) of a givencandidate in one list and candidates 62 in the other lists 60; andbetween known associates of a given candidate in one list and knownassociates of the candidates 62 in the other lists.

The example application of relationship detection thus enhances thebiometrics search results by finding non-obvious relationships betweencandidates biometrically identified with different biometric samples,such relationships otherwise potentially escaping notice. These detectedrelationships among the candidates can reveal, for example, members ofan illegal organization.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, and computer programproduct. Thus, aspects of the present invention may be embodied entirelyin hardware, entirely in software (including, but not limited to,firmware, program code, resident software, microcode), or in acombination of hardware and software. All such embodiments may generallybe referred to herein as a circuit, a module, or a system. In addition,aspects of the present invention may be in the form of a computerprogram product embodied in one or more computer readable media havingcomputer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wired, optical fiber cable, radio frequency (RF), etc. or any suitablecombination thereof.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as JAVA, Smalltalk, C#, C++, and Visual C++ or the like andconventional procedural programming languages, such as the C and Pascalprogramming languages or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The program code may execute entirely on a user's computer, partly onthe user's computer, as a stand-alone software package, partly on theuser's computer and partly on a remote computer or entirely on a remotecomputer or server. Any such remote computer may be connected to theuser's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider).

Aspects of the present invention are described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Aspects of the described invention may be implemented in one or moreintegrated circuit (IC) chips manufactured withsemiconductor-fabrication processes. The maker of the IC chips candistribute them in raw wafer form (on a single wafer with multipleunpackaged chips), as bare die, or in packaged form. When in packagedform, the IC chip is mounted in a single chip package, for example, aplastic carrier with leads affixed to a motherboard or other higherlevel carrier, or in a multichip package, for example, a ceramic carrierhaving surface and/or buried interconnections. The IC chip is thenintegrated with other chips, discrete circuit elements, and/or othersignal processing devices as part of either an intermediate product,such as a motherboard, or of an end product. The end product can be anyproduct that includes IC chips, ranging from electronic gaming systemsand other low-end applications to advanced computer products having adisplay, an input device, and a central processor.

Many modifications and variations will be apparent to those of ordinaryskill in the art without departing from the scope and spirit of theinvention. The embodiments were chosen and described in order to bestexplain the principles of the invention and the practical application,and to enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It is be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed.

While the invention has been shown and described with reference tospecific preferred embodiments, it should be understood by those skilledin the art that various changes in form and detail may be made thereinwithout departing from the spirit and scope of the invention as definedby the following claims.

What is claimed is:
 1. A method to enhance confidence in a biometric search result comprising: receiving, at an automated biometric identification system, one or more biometric samples of one or more persons of interest, providing, to a biometric search engine associated with one or more biometric records databases, the one or more biometric samples; identifying, in the biometric search engine, and in response to the provided one or more biometric samples, a plurality of individuals identified as potentially associated with the provided one or more biometric samples, wherein each identified individual is associated with a record including biographic information; receiving the biographic information associated with each of the identified individuals and user supplied biographic information about the one or more persons of interest at a relationship detection engine, the relationship detection engine identifying a relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; receiving, from the relationship detection engine, in response to submitted biographic information, at least one identified relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; and displaying, on a display screen, the identified relationship, wherein the identified relationship at least improves the biometric search result by identifying the relationship between identified individuals biometrically identified with different biometric samples.
 2. The method of claim 1, further comprising receiving biographic information about a known person of interest at the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the known person of interest.
 3. The method of claim 1, further comprising receiving biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest at the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the at least one known associate of the one or more persons of interest.
 4. The method of claim 1, further comprising: receiving biographic information about an associate of at least one of the identified individuals at the relationship detection engine; and receiving biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest at the relationship detection engine, wherein the identified relationship is between the at least one known associate of the one or more persons of interest and one of the at least one associate of the identified individuals.
 5. The method of claim 1, wherein the identified relationship is between one of the identified individuals associated with one biometric sample and one of the identified individuals associated with another biometric sample.
 6. The method of claim 1, further comprising receiving biographic information about associates of the identified individuals at the relationship detection engine, wherein the identified relationship is between one associate of one of the identified individuals and one associate of another of the identified individuals.
 7. A biometric identification based computing system comprising: memory storing an application program; and a processor running the application program stored in the memory, the application program configured to: receive, at an automated biometric identification system, one or more biometric samples of one or more persons of interest, provide, to a biometric search engine associated with one or more biometric records databases, the one or more biometric samples; identify, in the biometric search engine, and in response to the provided one or more biometric samples, a plurality of individuals identified as potentially associated with the provided one or more biometric samples, wherein each identified individual is associated with a record including biographic information; receive the biographic information associated with each of the identified individuals and user supplied biographic information about the one or more persons of interest at a relationship detection engine, the relationship detection engine identifying a relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; receive, from the relationship detection engine, in response to submitted biographic information, at least one identified relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; and display, on a display screen, the identified relationship, wherein the identified relationship at least improves the biometric search result by identifying the relationship between identified individuals biometrically identified with different biometric samples.
 8. The system of claim 7, wherein the application program is further configured to receive biographic information about a known person of interest to the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the known person of interest.
 9. The system of claim 7, wherein the application program is further configured to receive biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest to the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the at least one known associate of the one or more persons of interest.
 10. The system of claim 7, wherein the application program is further configured to: receive biographic information about an associate of at least one of the identified individuals to the relationship detection engine; and receive biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest to the relationship detection engine, wherein the identified relationship is between the at least one known associate of the one or more persons of interest and one of the at least one associate of the identified individuals.
 11. The system of claim 7, wherein the identified relationship is between one of the identified individuals associated with one biometric sample and one of the identified individuals associated with another biometric sample.
 12. The system of claim 7, wherein the application program is further configured to receive biographic information about associates of the identified individuals to the relationship detection engine, wherein the identified relationship is between one associate of one of the identified individuals and one associate of another of the identified individuals.
 13. The system of claim 7, wherein the system further comprises a hardware layer and a software layer, and the application program runs on the software layer and the hardware layer includes one or more connected elements, the elements including two or more of: storage, the display screen, a user input device, a signal bus, the memory, the processor and a network interface.
 14. A non-transitory computer readable information storage media having stored thereon instructions that enhance confidence in a biometric search result, the instructions, when executed by a processor, cause to be performed a method comprising: receiving, at an automated biometric identification system, one or more biometric samples of one or more persons of interest, providing, to a biometric search engine associated with one or more biometric records databases, the one or more biometric samples; identifying, in the biometric search engine, and in response to the provided one or more biometric samples, a plurality of individuals identified as potentially associated with the provided one or more biometric samples, wherein each identified individual is associated with a record including biographic information; receiving the biographic information associated with each of the identified individuals and user supplied biographic information about the one or more persons of interest at a relationship detection engine, the relationship detection engine identifying a relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; receiving, from the relationship detection engine, in response to submitted biographic information, at least one identified relationship between the one or more persons of interest for which the one or more biometric samples were submitted and the plurality of identified individuals; and displaying, on a display screen, the identified relationship, wherein the identified relationship at least improves the biometric search result by identifying the relationship between identified individuals biometrically identified with different biometric samples.
 15. The non-transitory computer readable information storage media of claim 14, further comprising computer readable program code configured to receive biographic information about a known person of interest to the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the known person of interest.
 16. The non-transitory computer readable information storage media of claim 14, further comprising computer readable program code configured to receive biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest to the relationship detection engine, wherein the identified relationship is between at least one of the identified individuals and the at least one known associate of the one or more persons of interest.
 17. The non-transitory computer readable information storage media of claim 14, further comprising: computer readable program code configured to receive biographic information about an associate of at least one of the identified individuals to the relationship detection engine; computer readable program code configured to receive biographic information about the one or more persons of interest and at least one known associate of the one or more persons of interest to the relationship detection engine, wherein the identified relationship is between the at least one known associate of the one or more persons of interest and one of the at least one associate of the identified individuals.
 18. The non-transitory computer readable information storage media of claim 14, wherein the identified relationship is between one of the identified individuals associated with one biometric sample and one of the identified individuals associated with another biometric sample.
 19. The non-transitory computer readable information storage media of claim 14, further comprising computer readable program code configured to receive biographic information about associates of the identified individuals to the relationship detection engine, wherein the identified relationship is between one associate of one of the identified individuals and one associate of another of the identified individuals. 